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8th Conference on Intelligent Autonomous Systems (IAS-8)

作者： Trianni, V Nolfi, S Dorigo, M Univ Libre Bruxelles IRIDIA Brussels Belgium CNR Inst Cognit Sci & Technol Rome Italy

In this paper, we Study coordinated motion in a swarm robotic system, called a swarm-bot. A swann-bot is a self-assembling and self-organising artifact, composed of a swarm of s-bots, mobile robots with the ability to connect to and disconnect from each other. The swann-bot concept is particularly suited for tasks that require all-terrain navigation abilities, such as space exploration or rescue in collapsed buildings. As a first step toward the development of more complex control strategies, we investigate the case in which a swarm-bot has to explore an arena while avoiding falling into holes. In such a scenario, individual s-bots have sensory-motor limitations that prevent them navigating efficiently. These limitations can be overcome if the s-bots are made to cooperate. In particular, we exploit the s-bots' ability to physically connect to each other. In order to synthesise the s-bots' controller, we rely on artificial evolution, which we show to be a powerful tool for the production of simple and effective solutions to the hole avoidance task. (c) 2005 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics swarm intelligence swarm robotics swarm-bot

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A Coevolutionary Approach to Learn Animal Behavior Through Controlled Interaction 13

A Coevolutionary Approach to Learn Animal Behavior Through C...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Li, Wei Gauci, Melvin Gross, Roderich Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9781450319638

This paper proposes a method that allows a machine to infer the behavior of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behavior. It is able to modify the environmental conditions and observe the animal;therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions.

关键词： Science automation animal behavior coevolution Turing test evolutionary robotics interaction artificial life

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Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms

Ensemble Feature Extraction for Multi-Container Quality-Dive...

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Cazenille, Leo Ochanomizu Univ Tokyo Japan

ISBN: (纸本)9781450383509

Quality-Diversity algorithms search for large collections of diverse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly dependent on the choice of feature descriptors (FDs) quantifying the similarity in behaviour of the solutions. While FDs usually needs to be hand-designed, recent studies have proposed ways to define them automatically by using feature extraction techniques, such as PCA or Auto-Encoders, to learn a representation of the problem from previously explored solutions. Here, we extend these approaches to more complex problems which cannot be efficiently explored by relying only on a single representation but require instead a set of diverse and complementary representations. We describe MC-AURORA, a Quality-Diversity approach that optimises simultaneously several collections of solutions, each with a different set of FDs, which are, in turn, defined automatically by an ensemble of modular auto-encoders. We show that this approach produces solutions that are more diverse than those produced by single-representation approaches.

关键词： Quality-Diversity algorithms AURORA evolutionary robotics Ensemble representation learning Auto-encoders Deep Learning

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Evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithm 05

Evolution of multi-loop controllers for fixed morphology wit...

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Genetic and evolutionary Computation Conference

作者： Parker, Gary Georgescu, Ramona Connecticut Coll Comp Sci Dept New London CT 06320 USA

ISBN: (纸本)1595930108

Cyclic genetic algorithms can be used to generate single loop control programs for robots. While successful in generating controllers for individual leg movement, gait generation, and area search path finding, cyclic genetic algorithms have had limited use when dealing with control problems that require different behaviors in response to sensor inputs. For such behaviors, there is a need for modifications that will allow the generation of multi-loop control programs, which can properly react to sensor input. In this work, we present modifications to the standard cyclic genetic algorithm that enables it to learn multi-loop control programs with branching that allows the control to jump from one loop to another. Preliminary tests show the success of our modification.

关键词： evolutionary robotics learning control code generation

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Spike-timing dependent plasticity for evolved robots

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ADAPTIVE BEHAVIOR 2002年第3-4期10卷 243-263页

作者： Di Paolo, EA Univ Sussex Sch Cognit & Comp Sci Brighton BN1 9QH E Sussex England

Plastic spiking neural networks are synthesized for phototactic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynaptic spikes at the millisecond range and on longer-term activity-dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural networks with low and high levels of neural noise. In all cases, the evolved controllers are highly robust against internal synaptic decay and other perturbations. The importance of the precise timing of spikes is demonstrated by randomizing the spike trains. In the low neural noise scenario, weight values undergo rhythmic changes at the mesoscale due to bursting, but during periods of high activity they are finely regulated at the microscale by synchronous or entrained firing. Spike train randomization results in loss of performance in this case. In contrast, in the high neural noise scenario, robots are robust to loss of information in the timing of the spike trains, demonstrating the counterintuitive results that plasticity, which is dependent on precise spike timing, can work even in its absence, provided the behavioral strategies make use of robust longer-term invariants of sensorimotor interaction. A comparison with a rate-based model of synaptic plasticity shows that under similarly noisy conditions, asymmetric spike-timing dependent plasticity achieves better performance by means of efficient reduction in weight variance over time. Performance also presents negative sensitivity to reduced levels of noise, showing that random firing has a functional value.

关键词： evolutionary robotics spiking neural networks spike-timing dependent plasticity activity-dependent synaptic scaling neural noise robustness

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Benefits of Proportionate Selection in Embodied Evolution: a Case Study with Behavioural Specialization 17

Benefits of Proportionate Selection in Embodied Evolution: a...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Bredeche, Nicolas Montanier, Jean-Marc Carrignon, Simon UPMC Univ Paris 06 CNRS ISIR Sorbonne Univ F-75005 Paris France Softbank Robot Europe Paris France Barcelona Supercomp Ctr BSC Barcelona Spain

ISBN: (纸本)9781450349390

We, as well as others, have already shown in previous works that reproductive isolation and a large population size are critical to achieve behavioral specialization in embodied evolutionary robotics. Here, we extend our previous work from [3] by experimentally demonstrating that fitness-proportionate and rank-based selection operators largely outperform other selection operators when it comes to evolving behavioral specialization.

关键词： evolutionary robotics embodied evolution swarm robotics

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Evolving Robust Solutions for Stochastically Varying Problems

Evolving Robust Solutions for Stochastically Varying Problem...

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IEEE Congress on evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Carvalho, Jonata Tyska Milano, Nicola Nolfi, Stefano Fundacao Univ Fed Rio Grande Ctr Computat Sci C3 Av ItaliaKm 8 Rio Grande Brazil CNR Natl Res Council Inst Cognit Sci & Technol Rome Italy CNR Inst Cognit Sci & Technol Rome Italy

ISBN: (纸本)9781509060177

We demonstrate how evaluating candidate solutions in a limited number of stochastically varying conditions that vary over generations at a moderate rate is an effective method for developing high quality robust solutions. Indeed, agents evolved with this method for the ability to solve an extended version of the double-pole balancing problem, in which the initial state of the agents and the characteristics of the environment in which the agents are situated vary, show the ability to solve the problem in a wide variety of environmental circumstances and for prolonged periods of time without the need to readapt. The combinatorial explosion of possible environmental conditions does not prevent the evolution of robust solutions. Indeed, exposing evolving agents to a limited number of different environmental conditions that vary over generations is sufficient and leads to better results with respect to control experiments in which the number of experienced environmental conditions is greater. Interestingly the exposure to environmental variations promotes the evolution of convergent strategies in which the agents act so to exhibit the required functionality and so to reduce the complexity of the control problem.

关键词： evolutionary robotics robustness environmental variation adaptive behaviour

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Evolving a Lookup Table Based Controller for Robotic Navigation

Evolving a Lookup Table Based Controller for Robotic Navigat...

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IEEE International Conference on Evolvable Systems (ICES)

作者： Beckerleg, Mark Matulich, Justin AUT Univ Sch Engn Auckland New Zealand

ISBN: (纸本)9781479944798

This paper describes how lookup tables can be evolved to control the motion of a simulated two wheeled robot, whose functions are either to move towards a light source or avoid obstacles. The robot has two light sensors, six obstacle sensors and two DC motor drivers for the wheels. The lookup table controls the motion of the robot by changing the motor speeds dependent on the sensor values. For light following, the axes of the table are right and left light sensor levels, whilst for obstacle avoidance the axis is the bit combination of the six digital sensors. The parameters within both tables are left and right motor direction. The genetic algorithm using two point crossover with a mutation rate of three percent and tournament selection successfully evolved the lookup tables for both navigational tasks.

关键词： evolutionary Computation evolutionary robotics Genetic Algorithms Light Following Robot Obstacle Avoidance Evolving a Lookup Table based Robotic Controller

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What is the relationship between behavioral robustness and distributed mechanisms of cognitive behavior?

What is the relationship between behavioral robustness and d...

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2010 IEEE World Congress on Computational Intelligence

作者： Fernandez-Leon, Jose A. Froese, Tom Univ Sussex CCNR Brighton BN1 9RH E Sussex England

ISBN: (纸本)9781424481262

There is a growing trend in the cognitive sciences to conceive of cognitive behavior as being distributed across brain, body and environment. However, the implications of such distribution for our understanding of biological robustness, which so far has been related to individual-based mechanisms alone, has rarely been discussed in the literature. We used the evolutionary robotics technique to examine the relationship between distributed behavioral mechanisms and behavioral robustness. Two kinds of model agents were evolved for a mobile object-tracking task and tested to see whether they can sustain their behavior despite sensorimotor perturbations. The results indicate that a highly distributed realization of behavior can be (i) detrimental, if it is mostly based on factors that are necessary for the behavior, or (ii) beneficial, if it is mostly based on factors that are sufficient for the behavior. Accordingly, we suggest that future discussions of distributed cognition should take into account that there are at least two different possible modes of realizing distributed behavior and that these have a qualitatively different effect on behavioral robustness.

关键词： behavioral robustness behavioural sciences computing biological robustness cognitive behavior cognitive sciences cognitive systems distributed mechanisms evolutionary robotics individual-based mechanisms mobile agents mobile object-tracking task sensorimotor perturbations

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Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation

Rapidly adapting robot swarms with Swarm Map-based Bayesian ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Bossens, David M. Tarapore, Danesh Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781728190778

Rapid performance recovery from un-foreseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion;and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm;for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.

关键词： Swarm robotics evolutionary robotics Bayesian optimisation

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